Integrating targeted genetic markers to genotyping-by-sequencing for an ultimate genotyping tool.

New selection methods, using trait-specific markers (marker-assisted selection (MAS)) and/or genome-wide markers (genomic selection (GS)), are becoming increasingly widespread in breeding programs. This new era requires innovative and cost-efficient solutions for genotyping. Reduction in sequencing cost has enhanced the use of high-throughput low-cost genotyping methods such as genotyping-by-sequencing (GBS) for genome-wide single-nucleotide polymorphism (SNP) profiling in large breeding populations. However, the major weakness of GBS methodologies is their inability to genotype targeted markers. Conversely, targeted methods, such as amplicon sequencing (AmpSeq), often face cost constraints, hindering genome-wide genotyping across a large cohort. Although GBS and AmpSeq data can be generated from the same sample, an efficient method to achieve this is lacking. In this study, we present the Genome-wide & Targeted Amplicon (GTA) genotyping platform, an innovative way to integrate multiplex targeted amplicons into the GBS library preparation to provide an all-in-one cost-effective genotyping solution to breeders and research communities. Custom primers were designed to target 23 and 36 high-value markers associated with key agronomical traits in soybean and barley, respectively. The resulting multiplex amplicons were compatible with the GBS library preparation enabling both GBS and targeted genotyping data to be produced efficiently and cost-effectively. To facilitate data analysis, we have introduced Fast-GBS.v3, a user-friendly bioinformatic pipeline that generates comprehensive outputs from data obtained following sequencing of GTA libraries. This high-throughput low-cost approach will greatly facilitate the application of DNA markers as it provides required markers for both MAS and GS in a single assay.

Identification of the genetic background of laboratory rats through amplicon-based next-generation sequencing for single-nucleotide polymorphism genotyping.

BACKGROUND: Laboratory rats, as model animals, have been extensively used in the fields of life science and medicine. It is crucial to routinely monitor the genetic background of laboratory rats. The conventional approach relies on gel electrophoresis and capillary electrophoresis (CE) technologies. However, the experimental and data analysis procedures for both of these methods are time consuming and costly. RESULTS: We established a single-nucleotide polymorphism (SNP) typing scheme using multiplex polymerase chain reaction (PCR) and next-generation sequencing (NGS) to address the genetic background ambiguity in laboratory rats. This methodology involved three rounds of PCR and two rounds of magnetic bead selection to improve the quality of the sequencing data. We simultaneously analysed 100 laboratory rats (including rats of 5 inbred strains and 2 in-house closed colonies), and the sequencing depth varied from an average of 108.25 to 5189.89, with sample uniformity ranging from 82.5 to 97.5%. A total of 98.9% of the amplicons were successfully genotyped (≥ 30 reads). Genetic background analysis revealed that all 38 experimental rats from the 5 inbred strains were successfully identified (without a heterozygous allele). For the 2 in-house closed colonies, the average heterozygosity (0.162 and 0.169) deviated from the typical range of 0.5-0.7, indicating a departure from the ideal heterozygosity level. Additionally, we employed multiplex PCR-CE to validate the NGS-based method, which yielded consistent results for all the rat strains. These results demonstrated that this approach significantly improves efficiency, saves time, reduces costs and ensures accuracy. CONCLUSION: By utilizing NGS technology, our developed method leverages SNP genotyping for genetic background identification in laboratory rats, demonstrating advantages in terms of labour efficiency and cost-effectiveness, thereby rendering it well suited for projects involving extensive sample cohorts.

Exome capture of Antarctic krill (Euphausia superba) for cost effective genotyping and population genetics with historical collections.

Antarctic krill (Euphausia superba Dana) is a keystone species in the Southern Ocean ecosystem, with ecological and commercial significance. However, its vulnerability to climate change requires an urgent investigation of its adaptive potential to future environmental conditions. Historical museum collections of krill from the early 20th century represent an ideal opportunity to investigate how krill have changed over time due to predation, fishing and climate change. However, there is currently no cost-effective method for implementing population scale collection genomics for krill given its genome size (48 Gbp). Here, we assessed the utility of two inexpensive methods for population genetics using historical krill samples, specifically low-coverage shotgun sequencing (i.e. 'genome-skimming') and exome capture. Two full-length transcriptomes were generated and used to identify 166 putative gene targets for exome capture bait design. A total of 20 historical krill samples were sequenced using shotgun and exome capture. Mitochondrial and nuclear ribosomal sequences were assembled from both low-coverage shotgun and off-target of exome capture data demonstrating that endogenous DNA sequences could be assembled from historical collections. Although, mitochondrial and ribosomal sequences are variable across individuals from different populations, phylogenetic analysis does not identify any population structure. We find exome capture provides approximately 4500-fold enrichment of sequencing targeted genes, suggesting this approach can generate the sequencing depth required to call identify a significant number of variants. Unlocking historical collections for genomic analyses using exome capture, will provide valuable insights into past and present biodiversity, resilience and adaptability of krill populations to climate change.

Evaluating the Efficacy of Target Capture Sequencing for Genotyping in Cattle.

(1) Background: Target capture sequencing (TCS) is potentially a cost-effective way to detect single-nucleotide polymorphisms (SNPs) and an alternative to SNP array-based genotyping. (2) Methods: We evaluated the effectiveness and reliability of TCS in cattle breeding scenarios using 48 female and 8 male samples. DNA was extracted from blood samples, targeted for 71,746 SNPs with TWIST probes, and sequenced on an MGI platform. GATK and BCFtools were evaluated for the best genotyping calling tool. The genotypes were compared to existing genotypes from the Versa50K SNP array of the same animals by measuring accuracy as concordance (%) and R2. (3) Results: In this study, 71,553 SNPs and 166 indels were identified. The genotype comparison of 37,130 common SNPs between TCS and SNP arrays yielded high agreement, with a mean concordance of 98%, R2 of 0.98 and Cohen's kappa of 0.97. The concordances of sex prediction, parent verification and validation of five genotype markers of interest important for Wagyu breeding were 100% between TCS and SNP array. The elements of the genomic relationship matrix (GRM) constructed from the SNP array and TCS data demonstrated a correlation coefficient approaching unity (r = 0.9998). (4) Conclusions: Compared to the SNP array, TCS is a comparable, cost-effective and flexible platform for genotyping SNPs, including non-model organisms and underrepresented commercial animal populations.

GenoTriplo: A SNP genotype calling method for triploids.

Triploidy is very useful in both aquaculture and some cultivated plants as the induced sterility helps to enhance growth and product quality, as well as acting as a barrier against the contamination of wild populations by escapees. To use genetic information from triploids for academic or breeding purposes, an efficient and robust method to genotype triploids is needed. We developed such a method for genotype calling from SNP arrays, and we implemented it in the R package named GenoTriplo. Our method requires no prior information on cluster positions and remains unaffected by shifted luminescence signals. The method relies on starting the clustering algorithm with an initial higher number of groups than expected from the ploidy level of the samples, followed by merging groups that are too close to each other to be considered as distinct genotypes. Accurate classification of SNPs is achieved through multiple thresholds of quality controls. We compared the performance of GenoTriplo with that of fitPoly, the only published method for triploid SNP genotyping with a free software access. This was assessed by comparing the genotypes generated by both methods for a dataset of 1232 triploid rainbow trout genotyped for 38,033 SNPs. The two methods were consistent for 89% of the genotypes, but for 26% of the SNPs, they exhibited a discrepancy in the number of different genotypes identified. For these SNPs, GenoTriplo had >95% concordance with fitPoly when fitPoly genotyped better. On the contrary, when GenoTriplo genotyped better, fitPoly had less than 50% concordance with GenoTriplo. GenoTriplo was more robust with less genotyping errors. It is also efficient at identifying low-frequency genotypes in the sample set. Finally, we assessed parentage assignment based on GenoTriplo genotyping and observed significant differences in mismatch rates between the best and second-best couples, indicating high confidence in the results. GenoTriplo could also be used to genotype diploids as well as individuals with higher ploidy level by adjusting a few input parameters.

GGTyper: genotyping complex structural variants using short-read sequencing data.

MOTIVATION: Complex structural variants (SVs) are genomic rearrangements that involve multiple segments of DNA. They contribute to human diversity and have been shown to cause Mendelian disease. Nevertheless, our abilities to analyse complex SVs are very limited. As opposed to deletions and other canonical types of SVs, there are no established tools that have explicitly been designed for analysing complex SVs. RESULTS: Here, we describe a new computational approach that we specifically designed for genotyping complex SVs in short-read sequenced genomes. Given a variant description, our approach computes genotype-specific probability distributions for observing aligned read pairs with a wide range of properties. Subsequently, these distributions can be used to efficiently determine the most likely genotype for any set of aligned read pairs observed in a sequenced genome. In addition, we use these distributions to compute a genotyping difficulty for a given variant, which predicts the amount of data needed to achieve a reliable call. Careful evaluation confirms that our approach outperforms other genotypers by making reliable genotype predictions across both simulated and real data. On up to 7829 human genomes, we achieve high concordance with population-genetic assumptions and expected inheritance patterns. On simulated data, we show that precision correlates well with our prediction of genotyping difficulty. This together with low memory and time requirements makes our approach well-suited for application in biomedical studies involving small to very large numbers of short-read sequenced genomes. AVAILABILITY AND IMPLEMENTATION: Source code is available at https://github.com/kehrlab/Complex-SV-Genotyping.

Wheat improvement through advances in single nucleotide polymorphism (SNP) detection and genotyping with a special emphasis on rust resistance.

KEY MESSAGE: Single nucleotide polymorphism (SNP) markers in wheat and their prospects in breeding with special reference to rust resistance. Single nucleotide polymorphism (SNP)-based markers are increasingly gaining momentum for screening and utilizing vital agronomic traits in wheat. To date, more than 260 million SNPs have been detected in modern cultivars and landraces of wheat. This rapid SNP discovery was made possible through the release of near-complete reference and pan-genome assemblies of wheat and its wild relatives, coupled with whole genome sequencing (WGS) of thousands of wheat accessions. Further, genotyping customized SNP sites were facilitated by a series of arrays (9 to 820Ks), a cost effective substitute WGS. Lately, germplasm-specific SNP arrays have been introduced to characterize novel traits and detect closely linked SNPs for marker-assisted breeding. Subsequently, the kompetitive allele-specific PCR (KASP) assay was introduced for rapid and large-scale screening of specific SNP markers. Moreover, with the advances and reduction in sequencing costs, ample opportunities arise for generating SNPs artificially through mutations and in combination with next-generation sequencing and comparative genomic analyses. In this review, we provide historical developments and prospects of SNP markers in wheat breeding with special reference to rust resistance where over 50 genetic loci have been characterized through SNP markers. Rust resistance is one of the most essential traits for wheat breeding as new strains of the Puccinia fungus, responsible for rust diseases, evolve frequently and globally.

Analysis of genotyping data reveals the unique genetic diversity represented by the breeds of sheep native to the United Kingdom.

BACKGROUND: Sheep breeds native to the United Kingdom exhibit a striking diversity of different traits. Some of these traits are highly sustainable, such as seasonal wool shedding in the Wiltshire Horn, and are likely to become more important as pressures on sheep production increase in coming decades. Despite their clear importance to the future of sheep farming, the genetic diversity of native UK sheep breeds is poorly characterised. This increases the risk of losing the ability to select for breed-specific traits from native breeds that might be important to the UK sheep sector in the future. Here, we use 50 K genotyping to perform preliminary analysis of breed relationships and genetic diversity within native UK sheep breeds, as a first step towards a comprehensive characterisation. This study generates novel data for thirteen native UK breeds, including six on the UK Breeds at Risk (BAR) list, and utilises existing data from the publicly available Sheep HapMap dataset to investigate population structure, heterozygosity and admixture. RESULTS: In this study the commercial breeds exhibited high levels of admixture, weaker population structure and had higher heterozygosity compared to the other native breeds, which generally tend to be more distinct, less admixed, and have lower genetic diversity and higher kinship coefficients. Some breeds including the Wiltshire Horn, Lincoln Longwool and Ryeland showed very little admixture at all, indicating a high level of breed integrity but potentially low genetic diversity. Population structure and admixture were strongly influenced by sample size and sample provenance - highlighting the need for equal sample sizes, sufficient numbers of individuals per breed, and sampling across multiple flocks. The genetic profiles both within and between breeds were highly complex for UK sheep, reflecting the complexity in the demographic history of these breeds. CONCLUSION: Our results highlight the utility of genotyping data for investigating breed diversity and genetic structure. They also suggest that routine generation of genotyping data would be very useful in informing conservation strategies for rare and declining breeds with small population sizes. We conclude that generating genetic resources for the sheep breeds that are native to the UK will help preserve the considerable genetic diversity represented by these breeds, and safe-guard this diversity as a valuable resource for the UK sheep sector to utilise in the face of future challenges.

Genotyping both live and dead animals to improve post-weaning survival of pigs in breeding programs.

BACKGROUND: In this study, we tested whether genotyping both live and dead animals (GSD) realises more genetic gain for post-weaning survival (PWS) in pigs compared to genotyping only live animals (GOS). METHODS: Stochastic simulation was used to estimate the rate of genetic gain realised by GSD and GOS at a 0.01 rate of pedigree-based inbreeding in three breeding schemes, which differed in PWS (95%, 90% and 50%) and litter size (6 and 10). Pedigree-based selection was conducted as a point of reference. Variance components were estimated and then estimated breeding values (EBV) were obtained in each breeding scheme using a linear or a threshold model. Selection was for a single trait, i.e. PWS with a heritability of 0.02 on the observed scale. The trait was simulated on the underlying scale and was recorded as binary (0/1). Selection candidates were genotyped and phenotyped before selection, with only live candidates eligible for selection. Genotyping strategies differed in the proportion of live and dead animals genotyped, but the phenotypes of all animals were used for predicting EBV of the selection candidates. RESULTS: Based on a 0.01 rate of pedigree-based inbreeding, GSD realised 14 to 33% more genetic gain than GOS for all breeding schemes depending on PWS and litter size. GSD increased the prediction accuracy of EBV for PWS by at least 14% compared to GOS. The use of a linear versus a threshold model did not have an impact on genetic gain for PWS regardless of the genotyping strategy and the bias of the EBV did not differ significantly among genotyping strategies. CONCLUSIONS: Genotyping both dead and live animals was more informative than genotyping only live animals to predict the EBV for PWS of selection candidates, but with marginal increases in genetic gain when the proportion of dead animals genotyped was 60% or greater. Therefore, it would be worthwhile to use genomic information on both live and more than 20% dead animals to compute EBV for the genetic improvement of PWS under the assumption that dead animals reflect increased liability on the underlying scale.

Multiplex PCR Approach for Rapid African Swine Fever Virus Genotyping.

African swine fever virus (ASFV) has been spreading through Europe, Asia, and the Caribbean after its introduction in Georgia in 2007 and, due to its particularly high mortality rate, poses a continuous threat to the pig industry. The golden standard to trace back the ASFV is whole genome sequencing, but it is a cost and time-intensive methodology. A more efficient way of tracing the virus is to amplify only specific genomic regions relevant for genotyping. This is mainly accomplished by amplifying single amplicons by PCR followed by Sanger sequencing. To reduce costs and processivity time, we evaluated a multiplex PCR based on the four primer sets routinely used for ASFV genotyping (B646L, E183L, B602L, and intergenic I73R-I329L), which was followed by Nanopore ligation-based amplicon sequencing. We show that with this protocol, we can genotype ASFV DNA originating from different biological matrices and correctly classify multiple genotypes and strains using a single PCR reaction. Further optimization of this method can be accomplished by adding or swapping the primer sets used for amplification based on the needs of a specific country or region, making it a versatile tool that can speed up the processing time and lower the costs of genotyping during ASFV outbreaks.